
# Load image from link
url = 'https://cdn.cbs.nl/images/6d6937674277573235627978534b49652b6e323757513d3d/900x450.jpg'

# Display image from URL with smaller size and subtitle
from IPython.display import Image, display

# Set the desired image width and height
width = 600
height = 300

# Set the subtitle text
subtitle = "© Hollandse Hoogte / Peter Hilz"

# Create an Image instance with the URL
image = Image(url=url, width=width, height=height)

# Display the image and subtitle
display(image)
print(subtitle)

© Hollandse Hoogte / Peter Hilz


import plotly.express as px
import pandas as pd

# Read the data from table1.csv
df = pd.read_csv('table1.csv')

# Create the line plot with Plotly
fig = px.line(df, x="Jaartal", y="Labour strikes", title='Number of labour strikes per year')

# Customize the plot layout
fig.update_layout(
    plot_bgcolor='white',
    height=400,
    xaxis=dict(title='Years'),
    yaxis=dict(title='')  # Remove y-axis tick labels
)

# Add blue small circles when hovering over a point
fig.update_traces(
    mode='lines+markers',
    hovertemplate='<b>%{x}</b><br>Labor strikes: <b>%{y}</b>' +
                  '<extra></extra>',  # Use <extra></extra> to disable default hover info
    line=dict(color='#00a1cd', width=2),
    marker=dict(color='#00a1cd', size=5),
)

# Add horizontal lines at y = 0, 10, 20, 30, and 40
y_values = [0, 10, 20, 30, 40]
for y in y_values:
    fig.add_shape(
        type='line',
        x0=min(df['Jaartal']),
        x1=max(df['Jaartal']),
        y0=y,
        y1=y,
        line=dict(color='black'),
        line_width=0.5
    )

# Show the plot
fig.show()


import plotly.graph_objects as go
import pandas as pd

# Read the data from table2.csv
df2 = pd.read_csv('table2.csv')

# Initialize a figure
fig2 = go.Figure()

# Create the first line
fig2.add_trace(
    go.Scatter(
        x=df2["Jaartal"], 
        y=df2["Workers involved (x 1,000)"],
        mode='lines+markers',
        line=dict(color='#00a1cd', width=2),
        marker=dict(color='#00a1cd', size=5),
        name="Workers involved",
        hovertemplate='<b>%{x}</b><br>Workers involved: <b>%{y} thousand</b> <extra></extra>'
    )
)

# Create the second line
fig2.add_trace(
    go.Scatter(
        x=df2["Jaartal"], 
        y=df2["Working days lost (x 1,000)"],
        mode='lines+markers',
        line=dict(color='#0058b8', width=2),
        marker=dict(color='#0058b8', size=5),
        name="Working days lost",
        hovertemplate='<b>%{x}</b><br>Working days lost: <b>%{y} thousand</b> <extra></extra>'
    )
)

# Customize the plot layout
fig2.update_layout(
    plot_bgcolor='white',
    xaxis=dict(title='Years'),
    yaxis=dict(title=''),  # Remove y-axis tick labels
    legend=dict(title='', orientation='h', yanchor='bottom', y=-0.3, xanchor='right', x=0.3),  # Update legend settings
    title="Number of workers involved and working<br>days lost during strikes (in thousand)",
    height=500
)

# Add horizontal lines at y = 0, 100, 200, 300, 400, and 500
y_values = [0, 100, 200, 300, 400, 500]
for y in y_values:
    fig2.add_shape(
        type='line',
        x0=min(df2['Jaartal']),
        x1=max(df2['Jaartal']),
        y0=y,
        y1=y,
        line=dict(color='black'),
        line_width=0.5
    )

# Show the plot
fig2.show()


import plotly.graph_objects as go
import pandas as pd

# Read the data from table3.csv
df3 = pd.read_csv('table3.csv')

# Create an empty figure
fig3 = go.Figure()

# Define a list of tuples with the variable names and their respective colors
variables = [("Collective agreement (CAO) (% (very) satisfied)", '#00a1cd'), 
             ("Salary (% (very) satisfied)", '#0058b8')]

# Loop over the variables to add a trace for each one
for var, color in variables:
    name = var.split("(")[0].strip()
    fig3.add_trace(go.Bar(
        y=df3["Bedrijfstak"],
        x=df3[var],
        name=name,
        orientation='h',
        hovertemplate=f'%{{y}}<br>{name}: <b>%{{x}}% (very) satisfied</b><extra></extra>',
        marker_color=color
    ))

# Customize the plot layout
fig3.update_layout(
    plot_bgcolor='white',
    title="Percentage of employees satisfied with<br>collective agreement and salary (15-74 yrs), 2022",
    xaxis=dict(title='', title_standoff=10, automargin=True, range=[0, 100]),  # Hide the original x-axis title
    yaxis=dict(title='', autorange="reversed", automargin=True),  # Remove y-axis title and add more whitespace between items
    legend=dict(title='', orientation='h', yanchor='bottom', y=-0.3, xanchor='right', x=0.04),  # Update legend settings
    barmode='group',  # Stack bars instead of placing them side-by-side
    height=600,
    annotations=[dict(x=1.015, y=-0.15, xref='paper', yref='paper', showarrow=False, text="(%) very satisfied")]  # Add annotation as x-axis title
)

# Show the plot
fig3.show()


import plotly.graph_objects as go
import pandas as pd
import numpy as np

# Assuming you have df3 as your original DataFrame
# Create a new DataFrame for random data
df4 = df3.copy()
# Set a random seed for reproducibility
np.random.seed(42)

# Add random data for years 2022-2026
for year in range(2022, 2027):
    for var, _ in variables:
        df4[var + " " + str(year)] = np.random.randint(0, 100, df4.shape[0])

# Create an empty figure
fig4 = go.Figure()

# Initialize a list to hold the trace names
trace_names = []

# Loop over the variables to add a trace for each one
for var, color in variables:
    for year in range(2022, 2027):
        name = var.split("(")[0].strip()
        trace_name = f"{name} {year}"
        fig4.add_trace(go.Bar(
            y=df4["Bedrijfstak"],
            x=df4[var + " " + str(year)],
            name=name,
            orientation='h',
            hovertemplate=f'%{{y}}<br>{name}: <b>%{{x}}% (very) satisfied</b><extra></extra>',
            marker_color=color,
            legendgroup=name,  # Add a legend group for each variable
            visible = year == 2022
        ))
        trace_names.append(trace_name)

# Add slider steps
steps = []
for i, year in enumerate(range(2022, 2027)):
    step = dict(
        method="update",
        args=[{"visible": [name.endswith(str(year)) for name in trace_names]},  # Show only the traces ending with the current year
              {"title": f"Randomized percentage of employees satisfied with collective agreement and salary<br>(15-74 yrs), {year}"}],
        label=str(year)
    )
    steps.append(step)

# Customize the plot layout
fig4.update_layout(
    plot_bgcolor='white',
    title="Randomized percentage of employees satisfied with<br>collective agreement and salary (15-74 yrs), 2022-2026",
    xaxis=dict(title='', title_standoff=10, automargin=True, range=[0, 100]),  
    yaxis=dict(title='', autorange="reversed", automargin=True),
    legend=dict(title='', orientation='h', yanchor='bottom', y=-0.185, xanchor='right', x=0.04),
    barmode='group',
    annotations=[dict(x=1.015, y=-0.15, xref='paper', yref='paper', showarrow=False, text="(%) very satisfied")],
    sliders=[dict(
        active=0,
        pad={"t": 110},  # Increase top padding for the slider to move it further down
        len=1,  # Adjust the length of the slider
        steps=steps,
        currentvalue={"visible": False}
    )],
    height=700
)

# Show the plot
fig4.show()


import plotly.graph_objects as go
import pandas as pd
import numpy as np
import plotly.colors as colors

# Set a random seed for reproducibility
np.random.seed(42)

# Create a new DataFrame for random data
df5 = df3[df3['Bedrijfstak'].isin(df3['Bedrijfstak'].unique()[:5])].copy()

# Define new variables and add random data for each one
new_variables = ["Work-life balance", "Job security", "Company culture"]
for var in new_variables:
    df5[var] = np.random.randint(6, 100, df5.shape[0])  # generate random integers from 6 to 100

# Initialize an empty figure
fig5 = go.Figure()

# Add a pie chart for each variable
for var in new_variables:
    fig5.add_trace(go.Pie(
        labels=df5["Bedrijfstak"],
        values=df5[var],
        name=var,
        hovertemplate=f'{var}: <b>%{{value}}% (very) satisfied</b><extra></extra>',
    ))

# Only display the first pie chart initially
for i in range(1, len(fig5.data)):
    fig5.data[i].visible = False

# Create a dropdown menu
dropdown = [{"label": var, "method": "update",
             "args": [{"visible": [j == i for j in range(len(new_variables))]},
                      {"title": f"Employee satisfaction with {var}"}]}
            for i, var in enumerate(new_variables)]


# Define a custom color scale
custom_colorscale = [
    '#2241D5',
    '#447ADE',
    '#66ABE6',
    '#88D2ED',
    '#AAEEF3'
]

# Customize the plot layout
fig5.update_layout(
    updatemenus=[
        go.layout.Updatemenu(
            buttons=dropdown,
            direction="down",
            showactive=True
        ),
    ],
    title="Employee satisfaction with Work-life balance",
    title_x=0.05,  # move the title to the left
    colorway=custom_colorscale,  # set the blue color scale
    height=500,  # set the height of the plot
    margin=dict(l=50, r=50, t=100, b=50),  # adjust the margins for proper spacing
    xaxis=dict(domain=[0, 0.45]),  # adjust the x-axis position
    yaxis=dict(domain=[0.2, 1.0])  # adjust the y-axis position
)

# Show the plot
fig5.show()


import geopandas as gpd
import pandas as pd
import plotly.express as px
import random

# Read the shapefile using geopandas
shapefile_path = 'gadm41_NLD_shp/gadm41_NLD_1.shp'
data = gpd.read_file(shapefile_path)

# Sample data for demonstration
sample_data = {
    'Province': ['Drenthe', 'Flevoland', 'Fryslân', 'Gelderland', 'Groningen', 'Limburg', 'Noord-Brabant', 'Noord-Holland', 'Overijssel', 'Utrecht', 'Zeeland', 'NA'],
    'average_income': [random.randint(0, 1000000) for _ in range(12)]
}

# Merge the sample data with the shapefile data
merged_data = data.merge(pd.DataFrame(sample_data), left_on='NAME_1', right_on='Province')

# Exclude Zeeuwse meren and IJsselmeer regions
merged_data = merged_data[~merged_data['NAME_1'].isin(['Zeeuwse meren', 'IJsselmeer'])]

# Rename Fryslân to Friesland
merged_data.loc[merged_data['NAME_1'] == 'Fryslân', 'NAME_1'] = 'Friesland'

# Rename Noord-Brabant to Brabant
merged_data.loc[merged_data['NAME_1'] == 'Noord-Brabant', 'NAME_1'] = 'Brabant'

# Rename NA to Zuid-Holland
merged_data.loc[merged_data['NAME_1'] == 'NA', 'NAME_1'] = 'Zuid-Holland'

# Create the choropleth map using Plotly Express
fig6 = px.choropleth_mapbox(
    merged_data,
    geojson=merged_data.geometry.__geo_interface__,
    color='average_income',
    locations=merged_data.index,
    featureidkey="id",
    center={"lat": 52.1326, "lon": 5.2913},
    mapbox_style="carto-positron",
    zoom=5.5,
    color_continuous_scale='Blues',
    labels={'average_income': 'Average income'},
    opacity=0.75,
    hover_name='NAME_1',  # Update to 'NAME_1' for correct hover labels
    hover_data={'average_income': True, 'NAME_1': False},  # Update to 'NAME_1' for correct hover data
    custom_data=['average_income'],
)

fig6.update_traces(
    hovertemplate='<b>%{hovertext}</b><br>Average income: <b>%{customdata[0]}k</b><extra></extra>',
)

fig6.update_layout(
    title_text='Average income by Province in the Netherlands',
    margin={"r": 30, "t": 70, "l": 30, "b": 30},
    legend=dict(
        traceorder='normal',
        font=dict(size=10),
        bgcolor='rgba(255, 255, 255, 0.5)',
        bordercolor='rgba(0, 0, 0, 0.5)',
        borderwidth=0.5
    ),
    hoverlabel=dict(
        bgcolor='white',
        font_size=12,
        font_family='Arial',
    ),
    height=600
)

fig6.show()

Analyzing the growth of Schiphol Airport¶

Introduction¶

Dataset and Preprocessing¶

Your First Perspective¶

The First Argument of Your First Perspective¶

The Second Argument of Your First Perspective¶

Your Second Perspective¶

The First Argument of Your Second Perspective¶

The Second Argument of Your Second Perspective¶

Reflection¶

Work Distribution¶

References¶